Applying Machine Learning Methods to Predict Crash Severity at Rural Roads- Case Study of Zanjan Province

Document Type : Original Article

Authors
Ali Tavakoli Kashani 1
Ali Medghalchi 2
Azam Mohammadi 3
Mohammad Jazvanaqi 4
1 Associate Professor, School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran.
2 School of Civil Engineering, Azad University of Qazvin, Qazvin, Iran.
3 School of Civil Engineering, Zanjan University, Zanjan, Iran.
4 M.Sc., Grad., Engineering, Imam Khomeini International University, Qzavin, Iran.
10.22034/road.2023.397089.2169
Abstract
Traffic crashes are a significant problem in low and middle-income countries, while there is a worrying trend of increasing fatal and injury crashes Iran. This highlights the urgent need to analyze the causes of such accidents to improve road safety and reduce their negative consequences. To address this issue, a study was conducted to investigate the factors that contribute to the severity of rural crashes in Zanjan province, using advanced machine learning models such as Support Vector Machine and Decision Tree. The study utilized a crash database of 25,000 incidents over a 9-year period, and after cleaning the data, the models were developed in Python. The findings suggest that “type of crash”, “at-fault driver's vehicle type”, and “kilometer occurrence of the crash” are key variables for predicting the severity of these crashes. The Decision Tree model was also found to be more accurate than the Support Vector Machine model, particularly in predicting severe crashes. This study provides valuable insights for improving road safety and reducing the harmful effects of traffic crashes in rural areas.
Keywords
Crash Severity
Decision Tree
Support Vector Machine
Rural Crashes
Traffic Safety
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